Facilitating concurrent forecasting of multiple time series

ABSTRACT

Embodiments of the present invention are directed to facilitating concurrent forecasting associating with multiple time series data sets. In accordance with aspects of the present disclosure, a request to perform a predictive analysis in association with multiple time series data sets is received. Thereafter, the request is parsed to identify each of the time series data sets to use in predictive analysis. For each time series data set, an object is initiated to perform the predictive analysis for the corresponding time series data set. Generally, the predictive analysis predicts expected outcomes based on the corresponding time series data set. Each object is concurrently executed to generate expected outcomes associated with the corresponding time series data set, and the expected outcomes associated with each of the corresponding time series data sets are provided for display.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/143,335, filed Apr. 29, 2016 and titled “CONCURRENTLY FORECASTINGMULTIPLE TIME SERIES,” which is itself a continuation-in-part of U.S.application Ser. No. 15/010,732, filed Jan. 29, 2016 and titled“ENHANCING TIME SERIES PREDICTION,” the contents of each of theforegoing applications are incorporated by reference herein in theirentirety.

BACKGROUND

Modern data centers often include thousands of hosts that operatecollectively to service requests from even larger numbers of remoteclients. During operation, components of these data centers can producesignificant volumes of raw, machine-generated data. In many cases, auser wishes to predict or forecast future values from such collecteddata.

SUMMARY

Embodiments of the present invention are related to facilitatingconcurrent forecasting associated with multiple time series data sets.In accordance with aspects of the present disclosure, a request toperform a predictive analysis in association with multiple time seriesdata sets is received. Thereafter, the request is parsed to identifyeach of the time series data sets to use in predictive analyses. Foreach time series data set, an object is initiated to perform thepredictive analysis for the corresponding time series data set.Generally, the predictive analysis predicts expected outcomes based onthe corresponding time series data set. Each object is concurrentlyexecuted to generate expected outcomes associated with the correspondingtime series data set, and the expected outcomes associated with each ofthe corresponding time series data sets are provided for display.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIGS. 7A-7D illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIG. 8 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments;

FIG. 9A illustrates a key indicators view in accordance with thedisclosed embodiments;

FIG. 9B illustrates an incident review dashboard in accordance with thedisclosed embodiments;

FIG. 9C illustrates a proactive monitoring tree in accordance with thedisclosed embodiments;

FIG. 9D illustrates a user interface screen displaying both log data andperformance data in accordance with the disclosed embodiments;

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system in which an embodiment may be implemented;

FIG. 11 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIGS. 12-14 illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIGS. 15-17 illustrate example visualizations generated by a reportingapplication in accordance with the disclosed embodiments;

FIG. 18A illustrates an exemplary user interface showing a portion of atime series data set with indications of values missing therefrom, inaccordance with embodiments of the present invention;

FIG. 18B illustrates an exemplary user interface showing a portion of atime series data set with predicted missing values and predictedexpected outcomes, in accordance with embodiments of the presentinvention;

FIG. 19 depicts a block diagram of an illustrative data processingenvironment in accordance with various embodiments of the presentdisclosure;

FIG. 20 illustrates an exemplary user interface showing a concurrentdisplay of forecasted values associated with multiple time series datasets, in accordance with embodiments of the present invention;

FIG. 21 is a flow diagram depicting an illustrative method of computingperiodicity utilizing predicted missing values, according to embodimentsof the present invention;

FIG. 22 is a flow diagram depicting another method of computingperiodicity utilizing predicted missing values, according to embodimentsof the present invention;

FIG. 23 is a flow diagram depicting an illustrative method of predictingexpected values, in accordance with embodiments of the presentinvention;

FIG. 24 is a flow diagram depicting an illustrative method offacilitating concurrent predictive analysis for multiple time seriesdata sets, in accordance with embodiments of the present invention;

FIG. 25 is a flow diagram depicting another illustrative method offacilitating concurrent predictive analysis for multiple time seriesdata sets, in accordance with embodiments of the present invention; and

FIG. 26 is a block diagram of an example computing device in whichembodiments of the present disclosure may be employed.

DETAILED DESCRIPTION

Embodiments are described herein according to the following outline:

1.0. General Overview 2.0. Operating Environment

2.1. Host Devices

2.2. Client Devices

2.3. Client Device Applications

2.4. Data Server System

2.5. Data Ingestion

-   -   2.5.1. Input    -   2.5.2. Parsing    -   2.5.3. Indexing

2.6. Query Processing

2.7. Field Extraction

2.8. Example Search Screen

2.9. Data Modeling

2.10. Acceleration Techniques

-   -   2.10.1. Aggregation Technique    -   2.10.2. Keyword Index    -   2.10.3. High Performance Analytics Store    -   2.10.4. Accelerating Report Generation

2.11. Security Features

2.12. Data Center Monitoring

2.13. Cloud-Based System Overview

2.14. Searching Externally Archived Data

-   -   2.14.1. ERP Process Features

3.0. Overview of Enhancing Time Series Prediction

3.1. Overview of a Predictive Analysis Tool in a Data ProcessingEnvironment

3.2. Illustrative Time Series Prediction Operations

3.3. Illustrative Hardware System

1.0 General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5).

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “SITE-BASED SEARCH AFFINITY”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in FIG.) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “|” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a search string. It also includes atime range picker 612 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, such as by selecting specifichosts and log files.

After the search is executed, the search screen 600 in FIG. 6A candisplay the results through search results tabs 604, wherein searchresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “selected fields” that are pre-selected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in FIG.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

FIGS. 12, 13, and 7A-7D illustrate a series of user interface screenswhere a user may select report generation options using data models. Thereport generation process may be driven by a predefined data modelobject, such as a data model object defined and/or saved via a reportingapplication or a data model object obtained from another source. A usercan load a saved data model object using a report editor. For example,the initial search query and fields used to drive the report editor maybe obtained from a data model object. The data model object that is usedto drive a report generation process may define a search and a set offields. Upon loading of the data model object, the report generationprocess may enable a user to use the fields (e.g., the fields defined bythe data model object) to define criteria for a report (e.g., filters,split rows/columns, aggregates, etc.) and the search may be used toidentify events (e.g., to identify events responsive to the search) usedto generate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. FIG. 12 illustrates an example interactive data modelselection graphical user interface 1200 of a report editor that displaysa listing of available data models 1201. The user may select one of thedata models 1202.

FIG. 13 illustrates an example data model object selection graphicaluser interface 1300 that displays available data objects 1301 for theselected data object model 1202. The user may select one of thedisplayed data model objects 1302 for use in driving the reportgeneration process.

Once a data model object is selected by the user, a user interfacescreen 700 shown in FIG. 7A may display an interactive listing ofautomatic field identification options 701 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 702, the “SelectedFields” option 703, or the “Coverage” option (e.g., fields with at leasta specified % of coverage) 704). If the user selects the “All Fields”option 702, all of the fields identified from the events that werereturned in response to an initial search query may be selected. Thatis, for example, all of the fields of the identified data model objectfields may be selected. If the user selects the “Selected Fields” option703, only the fields from the fields of the identified data model objectfields that are selected by the user may be used. If the user selectsthe “Coverage” option 704, only the fields of the identified data modelobject fields meeting a specified coverage criteria may be selected. Apercent coverage may refer to the percentage of events returned by theinitial search query that a given field appears in. Thus, for example,if an object dataset includes 10,000 events returned in response to aninitial search query, and the “avg_age” field appears in 854 of those10,000 events, then the “avg_age” field would have a coverage of 8.54%for that object dataset. If, for example, the user selects the“Coverage” option and specifies a coverage value of 2%, only fieldshaving a coverage value equal to or greater than 2% may be selected. Thenumber of fields corresponding to each selectable option may bedisplayed in association with each option. For example, “97” displayednext to the “All Fields” option 702 indicates that 97 fields will beselected if the “All Fields” option is selected. The “3” displayed nextto the “Selected Fields” option 703 indicates that 3 of the 97 fieldswill be selected if the “Selected Fields” option is selected. The “49”displayed next to the “Coverage” option 704 indicates that 49 of the 97fields (e.g., the 49 fields having a coverage of 2% or greater) will beselected if the “Coverage” option is selected. The number of fieldscorresponding to the “Coverage” option may be dynamically updated basedon the specified percent of coverage.

FIG. 7B illustrates an example graphical user interface screen (alsocalled the pivot interface) 705 displaying the reporting application's“Report Editor” page. The screen may display interactive elements fordefining various elements of a report. For example, the page includes a“Filters” element 706, a “Split Rows” element 707, a “Split Columns”element 708, and a “Column Values” element 709. The page may include alist of search results 711. In this example, the Split Rows element 707is expanded, revealing a listing of fields 710 that can be used todefine additional criteria (e.g., reporting criteria). The listing offields 710 may correspond to the selected fields (attributes). That is,the listing of fields 710 may list only the fields previously selected,either automatically and/or manually by a user. FIG. 7C illustrates aformatting dialogue 712 that may be displayed upon selecting a fieldfrom the listing of fields 710. The dialogue can be used to format thedisplay of the results of the selection (e.g., label the column to bedisplayed as “component”).

FIG. 7D illustrates an example graphical user interface screen 705including a table of results 713 based on the selected criteriaincluding splitting the rows by the “component” field. A column 714having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row) occurs in theset of events responsive to the initial search query.

FIG. 14 illustrates an example graphical user interface screen 1400 thatallows the user to filter search results and to perform statisticalanalysis on values extracted from specific fields in the set of events.In this example, the top ten product names ranked by price are selectedas a filter 1401 that causes the display of the ten most popularproducts sorted by price. Each row is displayed by product name andprice 1402. This results in each product displayed in a column labeled“product name” along with an associated price in a column labeled“price” 1406. Statistical analysis of other fields in the eventsassociated with the ten most popular products have been specified ascolumn values 1403. A count of the number of successful purchases foreach product is displayed in column 1404. This statistics may beproduced by filtering the search results by the product name, findingall occurrences of a successful purchase in a field within the eventsand generating a total of the number of occurrences. A sum of the totalsales is displayed in column 1405, which is a result of themultiplication of the price and the number of successful purchases foreach product.

The reporting application allows the user to create graphicalvisualizations of the statistics generated for a report. For example,FIG. 15 illustrates an example graphical user interface 1500 thatdisplays a set of components and associated statistics 1501. Thereporting application allows the user to select a visualization of thestatistics in a graph (e.g., bar chart, scatter plot, area chart, linechart, pie chart, radial gauge, marker gauge, filler gauge, etc.). FIG.16 illustrates an example of a bar chart visualization 1600 of an aspectof the statistical data 1501. FIG. 17 illustrates a scatter plotvisualization 1700 of an aspect of the statistical data 1501.

2.10. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.10.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 8 illustrates how a search query 802received from a client at a search head 210 can split into two phases,including: (1) subtasks 804 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 806 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 802, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 802 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 804, and then distributes searchquery 804 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4, or may alternativelydistribute a modified version (e.g., a more restricted version) of thesearch query to the search peers. In this example, the indexers areresponsible for producing the results and sending them to the searchhead. After the indexers return the results to the search head, thesearch head aggregates the received results 806 to form a single searchresult set. By executing the query in this manner, the systemeffectively distributes the computational operations across the indexerswhile minimizing data transfers.

2.10.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4, data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.10.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.10.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.11. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The App enables thesecurity practitioner to investigate and explore the data to find new orunknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional SIEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 9A illustrates anexample key indicators view 900 that comprises a dashboard, which candisplay a value 901, for various security-related metrics, such asmalware infections 902. It can also display a change in a metric value903, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 900 additionallydisplays a histogram panel 904 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 9B illustrates an example incident review dashboard 910 thatincludes a set of incident attribute fields 911 that, for example,enables a user to specify a time range field 912 for the displayedevents. It also includes a timeline 913 that graphically illustrates thenumber of incidents that occurred in time intervals over the selectedtime range. It additionally displays an events list 914 that enables auser to view a list of all of the notable events that match the criteriain the incident attributes fields 911. To facilitate identifyingpatterns among the notable events, each notable event can be associatedwith an urgency value (e.g., low, medium, high, critical), which isindicated in the incident review dashboard. The urgency value for adetected event can be determined based on the severity of the event andthe priority of the system component associated with the event.

2.12. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the developers's task to create variousapplications. One such application is SPLUNK® APP FOR VMWARE® thatprovides operational visibility into granular performance metrics, logs,tasks and events, and topology from hosts, virtual machines and virtualcenters. It empowers administrators with an accurate real-time pictureof the health of the environment, proactively identifying performanceand capacity bottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CORRELATIONFOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OFCOMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROMTHAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Example node-expansion operations are illustrated in FIG. 9C,wherein nodes 933 and 934 are selectively expanded. Note that nodes931-939 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/253,490, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 15 Apr. 2014, and U.S. patent application Ser. No.14/812,948, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 29 Jul. 2015, each of which is hereby incorporated byreference in its entirety for all purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous datacomprising events, log data, and associated performance metrics for theselected time range. For example, the screen illustrated in FIG. 9Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 942 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316, entitled“CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCEMETRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOGDATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan.2014, and which is hereby incorporated by reference in its entirety forall purposes.

2.13. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system. Similar to the system of FIG. 2, the networkedcomputer system 1000 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system1000, one or more forwarders 204 and client devices 1002 are coupled toa cloud-based data intake and query system 1006 via one or more networks1004. Network 1004 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 1002 and forwarders204 to access the system 1006. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 1006 forfurther processing.

In an embodiment, a cloud-based data intake and query system 1006 maycomprise a plurality of system instances 1008. In general, each systeminstance 1008 may include one or more computing resources managed by aprovider of the cloud-based system 1006 made available to a particularsubscriber. The computing resources comprising a system instance 1008may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 1002 to access a web portal orother interface that enables the subscriber to configure an instance1008.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 1008) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment such as SPLUNK® ENTERPRISE and a cloud-basedenvironment such as SPLUNK CLOUD™ are centrally visible).

2.14. Searching Externally Archived Data

FIG. 11 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 1104 over network connections1120. As discussed above, the data intake and query system 108 mayreside in an enterprise location, in the cloud, etc. FIG. 11 illustratesthat multiple client devices 1104 a, 1104 b, . . . , 1104 n maycommunicate with the data intake and query system 108. The clientdevices 1104 may communicate with the data intake and query system usinga variety of connections. For example, one client device in FIG. 11 isillustrated as communicating over an Internet (Web) protocol, anotherclient device is illustrated as communicating via a command lineinterface, and another client device is illustrated as communicating viaa system developer kit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 1104 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 1110. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 1110, 1112. FIG. 11 shows two ERP processes 1110, 1112 thatconnect to respective remote (external) virtual indices, which areindicated as a Hadoop or another system 1114 (e.g., Amazon S3, AmazonEMR, other Hadoop Compatible File Systems (HCFS), etc.) and a relationaldatabase management system (RDBMS) 1116. Other virtual indices mayinclude other file organizations and protocols, such as Structured QueryLanguage (SQL) and the like. The ellipses between the ERP processes1110, 1112 indicate optional additional ERP processes of the data intakeand query system 108. An ERP process may be a computer process that isinitiated or spawned by the search head 210 and is executed by thesearch data intake and query system 108. Alternatively or additionally,an ERP process may be a process spawned by the search head 210 on thesame or different host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 1110, 1112 receive a search request from the searchhead 210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 1110, 1112 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 1110, 1112 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 1110, 1112 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices1114, 1116, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 1104 may communicate with the data intake and querysystem 108 through a network interface 1120, e.g., one or more LANs,WANs, cellular networks, intranetworks, and/or internetworks using anyof wired, wireless, terrestrial microwave, satellite links, etc., andmay include the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.14.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the]streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

2.14. IT Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE™, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE™ system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE™ enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine, and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the SPLUNK® IT SERVICE INTELLIGENCE™ application. Each KPImeasures an aspect of service performance at a point in time or over aperiod of time (aspect KPI's). Each KPI is defined by a search querythat derives a KPI value from the machine data of events associated withthe entities that provide the service. Information in the entitydefinitions may be used to identify the appropriate events at the time aKPI is defined or whenever a KPI value is being determined. The KPIvalues derived over time may be stored to build a valuable repository ofcurrent and historical performance information for the service, and therepository, itself, may be subject to search query processing. AggregateKPIs may be defined to provide a measure of service performancecalculated from a set of service aspect KPI values; this aggregate mayeven be taken across defined timeframes and/or across multiple services.A particular service may have an aggregate KPI derived fromsubstantially all of the aspect KPI's of the service to indicate anoverall health score for the service.

SPLUNK® IT SERVICE INTELLIGENCE™ facilitates the production ofmeaningful aggregate KPI's through a system of KPI thresholds and statevalues. Different KPI definitions may produce values in differentranges, and so the same value may mean something very different from oneKPI definition to another. To address this, SPLUNK® IT SERVICEINTELLIGENCE™ implements a translation of individual KPI values to acommon domain of “state” values. For example, a KPI range of values maybe 1-100, or 50-275, while values in the state domain may be ‘critical,’‘warning,’ ‘normal,’ and ‘informational’. Thresholds associated with aparticular KPI definition determine ranges of values for that KPI thatcorrespond to the various state values. In one case, KPI values 95-100may be set to correspond to ‘critical’ in the state domain. KPI valuesfrom disparate KPI's can be processed uniformly once they are translatedinto the common state values using the thresholds. For example, “normal80% of the time” can be applied across various KPI's. To providemeaningful aggregate KPI's, a weighting value can be assigned to eachKPI so that its influence on the calculated aggregate KPI value isincreased or decreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE™ can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE™ can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be created and updated by an import of tabulardata (as represented in a CSV, another delimited file, or a search queryresult set). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in SPLUNK® IT SERVICE INTELLIGENCE™ can also be associatedwith a service by means of a service definition rule. Processing therule results in the matching entity definitions being associated withthe service definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE™ can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation search” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation search may additionally or alternativelycause a service ticket to be created in an IT service management (ITSM)system, such as a systems available from ServiceNow, Inc., of SantaClara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE™ provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation search, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE™ provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

3.0 Overview of Enhancing Time Series Predictions

Data is often collected as a time series data set, that is, a sequenceof data points, typically including successive measurements made over atime interval. Time series data is frequently utilized to perform apredictive or forecasting data analysis. In this manner, time seriesdata can be used to predict a future outcome based on the historicaltime series data. Expected outcomes can be predicted in relation to anytype or number of data. By way of example only, and without limitation,a user may desire forecasted data values for data center capacity,sales, license uses, etc.

Algorithms for time series forecasting can have many forms. For example,to perform time series forecasting, various algorithms using Kalmanfiltering can be implemented to forecast or predict expected valuesusing collected time series data. Generally, Kalman filtering refers toan algorithm that uses a series of measurements observed over time andproduces estimates of unknown variables. A Kalman filter model includesparameters that are adjusted or calculated using historical time seriesdata. Upon determining the parameters, the generated model can be usedto calculate future predictions. Any number or type of prediction modelscan be generated based on Kalman filtering. For example, a local levelalgorithm, a seasonal local level, a local level trend, a bivariatelocal level, or an algorithm combination can be based on the Kalmanfilter. At a high level, a local level algorithm refers to an algorithmhaving a univariate model with no or limited trends and seasonality. Aseasonal local level algorithm refers to an algorithm having aunivariate model with seasonality (periodicity of the time series iscomputed). A local level trend algorithm refers to an algorithm having aunivariate model with trend but no or minimal seasonality. A local levelbivariate algorithm refers to a bivariate model with no or limitedtrends and seasonality. Such an algorithm can use one set of data tomake predictions for another. For example, assume the bivariate modeluses dataset Y to make predictions for dataset X. If the holdback equalsten, the model takes the last ten data points of Y to make predictionsfor the last ten data points of X. A combination algorithm can be anycombination of the above or additional algorithms. For example, in oneembodiment, a combination algorithm may combine a seasonal local levelalgorithm and a local level trend algorithm to generate the combinedalgorithm. This combined algorithm benefits from utilizing theperiodicity in data of the seasonal local level algorithm and the trendin data of the local level trend algorithm. Although not describedherein, other combinations of algorithms can be utilized. Suchprediction models can then be used predict data associated with anynumber of future time intervals.

Typically, forecasting or prediction models generate more accuratepredictions when provided with more time series data points. In otherwords, the more historical data applied or analyzed, the more accuratethe prediction. In many cases, however, time series data is missing.Missing time series data can result in less accurate predictions orforecasts. As such, missing data might be predicted such that values forthe missing data can be used along with the previously recorded timeseries data to predict or forecast expected values.

Generally, predict commands are used to perform future predictions fortime series data. As such, a predict command initiates prediction orforecasting of expected values based on previously recorded time seriesdata. In addition to predicting a future value, a predict command canalso generate an upper and lower boundary or confidence level thatindicates a level of accuracy for the predicted value. For instance, thepredict command can predict upper and lower confidence intervals foreach predicted missing value that specify, for example, where 95% of thepredictions are expected to fall. As described herein, the predictcommand can be used to fill in or predict missing data in a time series.Both the observed time series data and the predicted missing values canbe used during the forecasting process. For example, predicted missingvalues can be used to compute periodicity in association with a timeseries data set. Periodicity generally refers to a tendency of data torecur at intervals. As such, to compute periodicity, data values,including predicted missing values, can be analyzed to determine aperiod over which the values recur at intervals, or values repeat inirregular intervals or periods. Predicted missing values can also beused, along with captured time series data, to predict or forecastfuture values.

By way of example, and with reference to FIGS. 18A and 18B, FIG. 18Aillustrates a captured time series data set 1802. In accordance withentering a predict command 1804, predicted missing values 1806 in FIG.18B are calculated and presented to visually illustrate the predictedmissing values that occur between the existing recorded time series dataset. As shown in FIG. 18A, the observed time series data set isdelineated with gaps or voids where missing values exist in the timeseries data, whereas the predicted missing values are included in thevisual pattern illustrated in FIG. 18B. As discussed herein, suchpredicted missing values may be used to compute periodicity. Upondetermining periodicity, historical time series data and predictedmissing values can then be used to forecast or predict future values1808 in FIG. 18B. As will be described herein, the predicted missingvalues to utilize for determining periodicity may be the same ordifferent as predicted missing values to forecast expected values. Inthis regard, one method for predicting missing values may be employedfor use in determining periodicity, while another method for predictingmissing values may be employed for use in forecasting expected values.

Further, embodiments described herein facilitate concurrent forecastingof multiple time series data sets. In particular, in response toreceiving a predict command indicating multiple time series data setsand, optionally, corresponding forecasting algorithms (and otherparameters), the predict command can be parsed to identify each of thetime series data sets. Based on the specified time series data sets andcorresponding parameters, an object is initiated for each specified timeseries data set to separately, but concurrently, perform predictiveanalysis. The results from the execution of the separate predictiveanalysis can be aggregated and provided to a requesting device fordisplay to a user such that the user can simultaneously view theforecasted results for the multiple time series data sets. As can beappreciated, initiating concurrent forecasting of multiple time seriesdata sets improves processing time that would otherwise be required toperform subsequent or serial predictive analysis for each time seriesdata set. In addition, a user can initiate predictive analyses formultiple time series data sets in a single query rather than initiatinga predictive analysis for each time series data set independently. Inresponse to the concurrent predictive analyses, the user can be providedwith a single view of forecasted results for multiple time series datasets, rather than being separately provided with forecasted results forvarious time series data sets.

3.1 Overview of a Predictive Analysis Tool in a Data ProcessingEnvironment

FIG. 19 illustrates an example data processing environment 1900 inaccordance with various embodiments of the present disclosure.Generally, the data processing environment 1900 refers to an environmentthat provides for, or enables, the management, storage, and retrieval ofdata to predict or forecast values expected to occur or result in thefuture. As shown in FIG. 19, the data processing environment includes apredictive analysis tool 1916 used to predict or forecast expectedoutcomes. The predictive analysis tool 1916 can utilize historical timeseries data to generate predicted outcomes expected in the future. Asdescribed herein, in addition to using historical time series data, thepredictive analysis tool 1916 uses predicted missing values ingenerating expected or forecasted values. Predicted missing values referto values that are predicted for data missing in the historical timeseries data set. As such, predicted missing values are generally valuesthat fall at points in time between recorded or observed values in atime series data set.

In some embodiments, the environment 1900 can include anevent-processing system 1902 communicatively coupled to one or moreclient devices 1904 and one or more data sources 1906 via acommunications network 1908. The network 1908 may include an element orsystem that facilitates communication between the entities of theenvironment 1900. The network 1908 may include an electroniccommunications network, such as the Internet, a local area network(LAN), a wide area network (WAN), a wireless local area network (WLAN),a cellular communications network, and/or the like. In some embodiments,the network 1908 can include a wired or a wireless network. In someembodiments, the network 1908 can include a single network or acombination of networks.

The data source 1906 may be a source of incoming source data 1910 beingfed into the event-processing system 1902. A data source 1906 can be orinclude one or more external data sources, such as web servers,application servers, databases, firewalls, routers, operating systems,and software applications that execute on computer systems, mobiledevices, sensors, and/or the like. Data source 1906 may be locatedremote from the event-processing system 1902. For example, a data source1906 may be defined on an agent computer operating remote from theevent-processing system 1902, such as on-site at a customer's location,that transmits source data 1910 to event-processing system 1902 via acommunications network (e.g., network 1908).

Source data 1910 can be a stream or set of data fed to an entity of theevent-processing system 1902, such as a forwarder (not shown) or anindexer 1912. In some embodiments, the source data 1910 can beheterogeneous machine-generated data received from various data sources1906, such as servers, databases, applications, networks, and/or thelike. Source data 1910 may include, for example raw data (e.g., rawtime-series data), such as server log files, activity log files,configuration files, messages, network packet data, performancemeasurements, sensor measurements, and/or the like. For example, sourcedata 1910 may include log data generated by a server during the normalcourse of operation (e.g. server log data). In some embodiments, thesource data 1910 may be minimally processed to generate minimallyprocessed source data. For example, the source data 1910 may be receivedfrom a data source 1906, such as a server. The source data 1910 may thenbe subjected to a small amount of processing to break the data intoevents. As discussed, an event generally refers to a portion, or asegment of the data, that is associated with a time. And, the resultingevents may be indexed (e.g., stored in a raw data file associated withan index file). In some embodiments, indexing the source data 1910 mayinclude additional processing, such as compression, replication, and/orthe like.

As can be appreciated, source data 1910 might be structured data orunstructured data. Structured data has a predefined format, whereinspecific data items with specific data formats reside at predefinedlocations in the data. For example, data contained in relationaldatabases and spreadsheets may be structured data sets. In contrast,unstructured data does not have a predefined format. This means thatunstructured data can comprise various data items having different datatypes that can reside at different locations.

The indexer 1912 of the event-processing system 1902 receives the sourcedata 1910, for example, from a forwarder (not shown) or the data source1906, and apportions the source data 1910 into events. An indexer 1912may be an entity of the event-processing system 1902 that indexes data,transforming source data 1910 into events and placing the results into adata store 1914, or index. Indexer 1912 may also search data stores 1914in response to requests or queries. An indexer 1912 may perform otherfunctions, such as data input and search management. In some cases,forwarders (not shown) handle data input, and forward the source data1910 to the indexers 1912 for indexing.

During indexing, and at a high-level, the indexer 1912 can facilitatetaking data from its origin in sources, such as log files and networkfeeds, to its transformation into searchable events that encapsulatevaluable knowledge. The indexer 1912 may acquire a raw data stream(e.g., source data 1910) from its source (e.g., data source 1906), breakit into blocks (e.g., 64K blocks of data), and/or annotate each blockwith metadata keys. After the data has been input, the data can beparsed. This can include, for example, identifying event boundaries,identifying event timestamps (or creating them if they don't exist),masking sensitive event data (such as credit card or social securitynumbers), applying custom metadata to incoming events, and/or the like.Accordingly, the raw data may be data broken into individual events. Theparsed data (also referred to as “events”) may be written to a datastore, such as an index or data store 1914.

The data store 1914 may include a medium for the storage of datathereon. For example, data store 1914 may include non-transitorycomputer-readable medium storing data thereon that is accessible byentities of the environment 1900, such as the corresponding indexer 1912and the predictive analysis tool 1916. As can be appreciated, the datastore 1914 may store the data (e.g., events) in any manner. In someimplementations, the data may include one or more indexes including oneor more buckets, and the buckets may include an index file and/or rawdata file (e.g., including parsed, time-stamped events). In someembodiments, each data store is managed by a given indexer that storesdata to the data store and/or performs searches of the data stored onthe data store. Although certain embodiments are described with regardto a single data store 1914 for purposes of illustration, embodimentsmay include employing multiple data stores 1914, such as a plurality ofdistributed data stores 1914.

As described, events within the data store 1914 may be represented by adata structure that is associated with a certain point in time andincludes a portion of raw machine data (e.g., a portion ofmachine-generated data that has not been manipulated). An event mayinclude, for example, a line of data that includes a time reference(e.g., a timestamp), and one or more other values. In the context ofserver log data, for example, an event may correspond to a log entry fora client request and include the following values: (a) a time value(e.g., including a value for the data and time of the request, such as atimestamp), and (b) a series of other values including, for example, apage value (e.g., including a value representing the page requested), anIP (Internet Protocol) value (e.g., including a value for representingthe client IP address associated with the request), and an HTTP(Hypertext Transfer protocol) code value (e.g., including a valuerepresentative of an HTTP status code), and/or the like. That is, eachevent may be associated with one or more values. Some events may beassociated with default values, such as a host value, a source value, asource type value and/or a time value. A default value may be common tosome of all events of a set of source data.

In some embodiments, an event can be associated with one or morecharacteristics that are not represented by the data initially containedin the raw data, such as characteristics of the host, the source, and/orthe source type associated with the event. In the context of server logdata, for example, if an event corresponds to a log entry received fromServer A, the host and the source of the event may be identified asServer A, and the source type may be determined to be “server.” In someembodiments, values representative of the characteristics may be addedto (or otherwise associated with) the event. In the context of serverlog data, for example, if an event is received from Server A, a hostvalue (e.g., including a value representative of Server A), a sourcevalue (e.g., including a value representative of Server A), and a sourcetype value (e.g., including a value representative of a “server”) may beappended to (or otherwise associated with) the corresponding event.

In some embodiments, events can correspond to data that is generated ona regular basis and/or in response to the occurrence of a given event.In the context of server log data, for example, a server that logsactivity every second may generate a log entry every second, and the logentries may be stored as corresponding events of the source data.Similarly, a server that logs data upon the occurrence of an error eventmay generate a log entry each time an error occurs, and the log entriesmay be stored as corresponding events of the source data.

In accordance with events being stored in the data store 1914, thepredictive analysis tool 1916 can function to predict or forecastoutcomes expected to occur in the future. Although the predictiveanalysis tool 1916 is illustrated and described herein as a separatecomponent, this is for illustrative purposes. As can be appreciated, thepredictive analysis tool 1916, or functions described in associationtherewith, can be performed at the indexer 1912, a search head (notshown), or any other component. For example, some functionalitydescribed in association with the predictive analysis tool 1916 might beperformed at a search head, while other functionality described inassociation with the predictive analysis tool 1916 might be performed atan indexer.

As described herein, the predictive analysis tool 1916 can be initiatedby a user of the client device 1904. The client device 1904 may be usedor otherwise accessed by a user 1922, such as a system administrator ora customer. A client device 1904 may include any variety of electronicdevices. In some embodiments, a client device 1904 can include a devicecapable of communicating information via the network 1908. A clientdevice 1904 may include one or more computer devices, such as a desktopcomputer, a server, a laptop computer, a tablet computer, a wearablecomputer device, a personal digital assistant (PDA), a smart phone,and/or the like. In some embodiments, a client device 1904 may be aclient of the event processing system 1902. In some embodiments, aclient device 1904 can include various input/output (I/O) interfaces,such as a display (e.g., for displaying a graphical user interface(GUI), an audible output user interface (e.g., a speaker), an audibleinput user interface (e.g., a microphone), an image acquisitioninterface (e.g., a camera), a keyboard, a pointer/selection device(e.g., a mouse, a trackball, a touchpad, a touchscreen, a gesturecapture or detecting device, or a stylus), and/or the like. In someembodiments, a client device 1904 can include general computingcomponents and/or embedded systems optimized with specific componentsfor performing specific tasks. In some embodiments, a client device 1904can include programs/applications that can be used to generate a requestfor content, to provide content, to render content, and/or to sendand/or receive requests to and/or from other devices via the network1908. For example, a client device 1904 may include an Internet browserapplication that facilitates communication with the event-processingsystem 1902 via the network 1908. In some embodiments, a program, orapplication, of a client device 1904 can include program modules havingprogram instructions that are executable by a computer system to performsome or all of the functionality described herein with regard to atleast client device 1904. In some embodiments, a client device 1904 caninclude one or more computer systems similar to that of the computersystem 2600 described below with regard to at least FIG. 26.

Prediction analysis can be initiated or triggered at the client device1904 via a search graphical user interface (GUI). In some embodiments,the event-processing system 1902 can provide for the display of a searchGUI. Such a search GUI can be displayed on a client device 1904, and canpresent information relating to initiating prediction analysis,performing prediction analysis, and viewing results of predictionanalysis.

Prediction analysis can be initiated at a client device by a user at anytime. In this regard, a user may initiate prediction analysis prior toor in accordance with performing a search for information. Althoughgenerally described herein as performing prediction analysis upon theevents being created, indexed, and stored, prediction analysis can bedefined and applied before or as events are created, indexed, and/orstored. Further, prediction analysis may be automatically triggered. Forexample, upon initially establishing a prediction analysis, subsequentprediction analyses may be automatically triggered and performed as newdata is received.

The prediction analysis tool 1916 is generally configured to facilitateprediction analysis. As described, prediction analysis can be initiatedor triggered in response to a user selection or input to predict orforecast data values expected to occur in the future. In this regard, auser may enter a predict command, for example, via client device 1904 totrigger forecasting of future data. An exemplary predict command 1804 isillustrated in FIG. 18A. In accordance with entering a predict command,or otherwise selecting to initiate prediction analysis, a user canspecify additional parameters that can be used to predict or forecastdata values. For example, a user might input or select a field name fora variable desired to be predicted, a forecasting algorithm desired foruse in predicting outcomes, a timespan or length of time for predictingvalues into the future (e.g., predict values for the next 6 months), atime interval for predicting values (e.g., predict values for each day),a holdback indicating a number of data points from the end of the timeseries that are not desired for use in building the forecasting model, adesired confidence level, or the like. Although various parameters mightbe input by a user, as can be appreciated, in some implementations, anynumber of such parameters might be default parameters that are used. Forexample, a default forecasting algorithm might be used or a defaultconfidence level (e.g., 95%) might be used for forecasting data.

Upon the prediction analysis tool 1916 receiving or identifying anindication to initiate prediction analysis, the prediction analysis tool1916 can access the appropriate time series data for use performing theprediction of expected outcomes. The appropriate time series data toanalyze might be indicated, for instance, in the predict command inputby a user. As such, the predict command can be parsed to identifyvarious parameters and options related to forecasting, such as, forexample, a set of time series data, a prediction time span, an amount ofhistorical data to use for forecasting, a forecasting algorithm toutilize, etc. In embodiments, the time series data can be referencedfrom the data store 1914, for example.

As can be appreciated, in some cases, data initially stored in the datastore 1914 might not be in a time series data format. For example, rawdata stored in the data store 1914 may not be in a time series dataformat. In such a case, prior to performing prediction analysis topredict or forecast expected values, the data can be converted to a timeseries data set. One example of converting a set of data into a timeseries data format includes using a timechart command, or other similarfunctionality. Generally, the timechart command generates a table ofsummary statistics. Such data can then be formatted as a chartvisualization where the data is plotted against an x-axis thatrepresents a time field. A timechart command may be used to displaystatistical trends over time, with the option of splitting the data withanother field as a separate series in the chart. Timechartvisualizations are generally line, area, or column charts.

A timechart command, or other similar functionality, used to convertdata into a time series data format can be applied automatically orbased on a user selection. For example, upon receiving a predictcommand, a determination may be made that the corresponding data to usefor the predictive analysis is not in a time series data format. Assuch, a timechart command can be initiated to convert the data into atime series data format. As another example, a user may select or entera timechart command to convert data into a time series data format suchthat it is in a format that can be used for data forecasting. Further,conversion of data into a time series data format may occur at any time.For instance, it may occur in association with a selection to perform apredictive analysis, or at a time prior to a selection to perform apredictive analysis. In cases that time series data is not alreadystored in the data store 1914 for use in predictive analysis, the timeseries data as determined in accordance with the timechart command canbe stored in the data store 1914 for subsequent utilization.

Upon accessing appropriate time series data, the time series data can beanalyzed to detect or determine any missing values. Missing values canbe detected in any manner and is not intended to be limited to examplesprovided herein. In an embodiment when input data is converted to a timeseries data format, such as a table, entries without any values for aparticular field can be considered missing in relation to that field. Asanother example, missing values might be detected when a value ismissing in relation to a particular time instance. For example, assumethat values are in a time series data set in association with day 1, day2, and day 4. In such a case, it can be determined that a value ismissing in relation to day 3.

For the values determined to be missing, a prediction of the missingvalue can be made. That is, predictions are generated for missing values(generally referred to herein as predicted missing values). A predictedmissing value refers to a predicted or forecasted value associated witha time instance that is missing a collected or observed value. Predictedmissing values can be generated based on observed time series data. Assuch, data previously collected can be used to predict missing values.

In some embodiments, missing values are predicted based on neighboringor nearby time series data. In some cases, a predicted missing value canbe determined based on an average, or weighted average, of itsneighboring time series data. In this case, the predicted missing valuescan reflect the trend of the time series data observed before and afterthe missing data points. One exemplary algorithm used to generatepredicted missing values is provided below. In the below algorithm,assume that a and b represent observed time series data, while thefeatures in between the a and b time series data represent missingvalues (represented as MV₁, MV₂, MV₃, MV₄). In such a case, the missingvalues MV₁, MV₂, MV₃, MV₄ can be calculated as follows:

$a,\frac{{na} + b}{n + 1},\frac{{\left( {n - 1} \right)a} + {2b}}{n + 1},{\frac{{\left( {n - 2} \right)a} + {3b}}{n + 1}\mspace{11mu} \ldots \mspace{11mu} \frac{a + {nb}}{n + 1}},b$MV₁  MV₂  MV₃  MV_(n)

wherein n denotes the number of missing values between time series dataa and b. Such an implementation effectively weights the values based onnearness or proximity to the neighboring value. As a specific example,assume that four missing values are detected MV₁, MV₂, MV₃, MV₄ betweena and b time series data. In such a case, the predicted missing valuescan be determined as:

$a,\frac{{4a} + b}{5},\frac{{3a} + {2b}}{5},\frac{{2a} + {3b}}{5},\frac{a + {4b}}{5},b$MV₁  MV₂  MV₃  MV₄

As can be appreciated, other algorithms that predict missing valuesbased on neighboring observed time series data are contemplated withinthe scope of embodiments of the present invention. For example, apredicted missing value can be determined using neighboring time seriesdata in a manner other than using an average, or weighted average,calculation. Further, weights can be applied in other manners todetermine the missing values.

Upon generating predicted missing values, the predicted missing valuescan be used along with the observed time series data to determineperiodicity associated with the data. Based on a sequence of values,periodicity is determined to identify a period or cycle associated withthe sequence of values. Determining periodicity associated with a set ofdata, such as observed time series data and predicted missing values,can be performed in any number of ways and is not intended to be limitedin scope to method described herein.

In some embodiments, autocorrelation can be used to determineperiodicity. Autocorrelation generally refers to cross-correlation of asignal with itself at different points in time. As such, autocorrelationrepresents the similarity between observations as a function of the timelag between them. Autocorrelation can be used to identify repeatingpatterns, such as a periodic pattern, for example among a series ofvalues. In implementation, autocorrelations can be determined for anynumber of lags. A lag generally refers to a period of time or timeinterval between one event and another (e.g., one data value toanother). Autocorrelations of a lag can be generally represented as:

Autocorrelation=a ₁ a _(1+lag) +a ₂ a _(2+lag) + . . . +a _(n) a_(n)+lag

wherein a₁ represents a first value in a set of data.

In this regard, the autocorrelation of lag 1 is represented as follows:

Autocorrelation (lag 1)=a ₁ a ₂ +a ₂ a ₃ +a ₃ a ₄ + . . . +a _(n) a_(n+1)+ . . .

wherein a₁ represents a first value at a first time in a set of data, a₂represents a second value at a second time in a set of data, a₃represents a third value at a third time in a set of data, a₄ representsa fourth value at a fourth time in a set of data, and an represents anth value at a nth time in a set of data.

Similarly, the autocorrelation of lag 2 is represented as follows:

Autocorrelation (lag 2)=a ₁ a ₃ +a ₂ a ₄ +a ₃ a ₅ + . . . +a _(n) a_(n+2)+ . . .

wherein a₁ represents a first value at a first time in a set of data, a₂represents a second value at a second time in a set of data, a₃represents a third value at a third time in a set of data, a₄ representsa fourth value at a fourth time in a set of data, a₅ represents a fifthvalue at a fifth time in a set of data, and a_(n) represents a nth valueat a nth time in a set of data.

As such, given a set of data, such as observed time series data andpredicted missing values, an autocorrelation value can be determined forany number of lags associated with the data. In some embodiments,autocorrelation values are determined for each possible lag value fromthe first value in the data to the last value in the data set, that is,all lags up to the length of the sequence. In other embodiments,autocorrelation values are determined up to a threshold number of lags.A threshold number can represent the maximum lag value for whichautocorrelation is determined. As an example, a threshold number of lagvalues might equal 2000. In such a case, autocorrelation values aredetermine for lags 1 to 2000. Such a threshold number (e.g., 2000) maybe selected in an effort to maintain performance and accuracy incomputing the period. Utilizing a lag threshold can greatly increasecomputation efficiency, particularly for large data sets. For example,determining autocorrelations of lags from 1 up to the length of the datacan result in quadratic time complexity and, as such, decrease theprocessing speed for a predict command, particularly for large datasets.

Upon determining autocorrelation values for a sequence of data, theautocorrelation values can be searched to identify the greatest orlargest autocorrelation value. Stated differently, the autocorrelationvalues can be analyzed to identify the lag that yields the maximumautocorrelation. In accordance with embodiments of the presentinvention, the lag associated with the greatest autocorrelation valuecan be used to designate the periodicity. For example, assume that lag 3is identified as being associated with the largest autocorrelationvalue. In such a case, the value of 3 is designated as the periodicity.As another example, assume that the lag 3 is identified as beingassociated with the largest autocorrelation value. In such a case, thelag plus 1 can be designated as the periodicity (in this case the lag of3 plus 1 results in a periodicity of 4). As can be appreciated, theperiodicity value (e.g., 3 or 4 in these examples) can correspond withany amount of time, such as minutes, hours, days, weeks, etc.

In some cases, prior to designating a lag associated with a largestautocorrelation value as a periodicity for the data, the determinedautocorrelation can be compared to a threshold autocorrelation todetermine whether to signify the data as being periodic. By way ofexample only, assume that a threshold autocorrelation of 0.01 isdesignated. In such a case, if a maximum or greatest correlation is atleast 0.01, the corresponding lag value can be returned as a periodassociated with the data. On the other hand, if the maximum or greatestcorrelation is equal to or less than 0.01, a value of −1 is returned toindicate that the data is not periodic.

As described, the determined periodicity can be used in association witha forecasting model that considers periodicity to forecast or predictvalues expected to occur in the future. As can be appreciated, anynumber of forecasting models using periodicity can be implemented toprovide predictions. Further, in some cases, when forecasting models arecombined to predict expected outcomes, one or more of such forecastingmodels might use periodicity in predicting expected outcomes.

Algorithms for time series forecasting can have many forms. For example,to perform time series forecasting, various algorithms using Kalmanfiltering can be implemented to forecast or predict expected valuesusing collected time series data. Generally, Kalman filtering refers toan algorithm that uses a series of measurements observed over time andproduces estimates of unknown variables. A Kalman filter model includesparameters that are adjusted or calculated using historical time seriesdata. Upon determining the parameters, the generated model can be usedto calculate future predictions.

As described, generating parameters for forecasting models can beperformed based on historical time series data. Because more datagenerally results in a more accurate prediction, missing values can bepredicted and used along with historical time series data to determineparameters for a forecasting model(s). Missing values can be predictedin any number of ways. For example, in some embodiments, predictedmissing values are generated as described above. In this regard, thepredicted missing values are determined based on neighbor values. Inother embodiments, a different method for determining predicted missingvalues may be employed. For instance, a Kalman filter recursion can beexecuted for each missing data point, wherein the output is used to fillin the missing data point.

The resulting predicted missing values and/or forecasted expected valuescan be provided by the prediction analysis tool 1916 for presentation tothe user, for example, via the client device. By way of example, andwith reference to FIGS. 18A and 18B, FIG. 18A illustrates a capturedtime series data set 1802. In accordance with entering a predict command1804, predicted missing values 1806 in FIG. 18B are calculated andpresented to visually illustrate the predicted missing values that occurbetween the existing recorded time series data set. As shown in FIG.18A, the observed time series data set is delineated with gaps or voidswhere missing values exist in the time series data, whereas thepredicted missing values are included in the visual pattern illustratedin FIG. 18B. As discussed herein, such predicted missing values may beused to compute periodicity and/or to forecast future values. Historicaltime series data and predicted missing values can then be used toforecast or predict future values 1808 in FIG. 18B.

Returning to FIG. 19, in some embodiments, the prediction analysis tool1916 enables concurrent forecasting in association with multiple timeseries data sets. In this regard, rather than the performing forecastingfor only a single time series data set, forecasting for multiple timeseries data sets can be performed at the same time.

The prediction analysis tool 1916 can facilitate concurrent predictionanalysis of multiple time series data sets. Prediction analysis ofmultiple time series data sets can be initiated or triggered in responseto a user selection or input to concurrently predict or forecast datavalues expected to occur in the future in association with multiple timeseries data sets. In this regard, a user may enter a predict command,for example, via client device 1904 to trigger forecasting of futuredata for multiple time series data sets. An exemplary predict command2002 is illustrated in FIG. 20. In accordance with entering a predictcommand, or otherwise selecting to initiate prediction analysis, a usercan specify multiple time series data sets to use for forecasting.Further, additional parameters can be specified that can be used topredict or forecast data values associated with multiple time seriesdata sets. For example, a user might input or select a field name for avariable desired to be predicted, a forecasting algorithm desired foruse in predicting outcomes, a timespan or length of time for predictingvalues into the future (e.g., predict values for the next 6 months), atime interval for predicting values (e.g., predict values for each day),a holdback indicating a number of data points from the end of the timeseries that are not desired for use in building the forecasting model, adesired confidence level, or the like.

As can be appreciated, in one embodiment, a particular forecastingalgorithm can be selected to be applied to each of the desired timeseries data sets. For instance, a set of five time series data sets maybe indicated for performing concurrent prediction analysis with only asingle forecasting algorithm set forth. In other embodiments,forecasting algorithms can be selected for the different time seriesdata set. For instance, a different forecasting algorithm can bedesignated for each specified time series data set. As an example, a setof five time series data sets may be indicated with a differentforecasting model specified for each data set.

By way of example only, the exemplary predict command 2002 illustratedin FIG. 20 can initiate concurrent forecasting of values expected inassociation with two time series data sets, namely “count(Fin)” timeseries data set 2004 and “Nor” time series data set 2006. Eachindication of the time series data set for forecasting expected valuesalso includes various parameters for performing prediction analysis. Forinstance, both time series data sets 2004 and 2006 are respectivelyassociated with forecasting algorithms 2008 and 2010, holdbacks 2012 and2014, and timespan 2016 and 2018. Such an input can initiate concurrentdata forecasting in association with the “count (Fin)” time series dataset and the “Nor” time series data set using the specified correspondingparameters (e.g., algorithm, etc.).

An indication of time series data sets and associated parameters (e.g.,forecasting algorithms) can be specified in any manner. Although FIG. 20illustrates the time series data sets and associated parameters as beinginput via a query input box 2020, as can be appreciated, in someimplementations, such selections can be made in other manners. Forinstance, selection or drop down menus can be employed to makeselections of time series data sets, algorithms, or other parameters.Further, although various parameters might be input by a user, as can beappreciated, in some implementations, any number of such parametersmight be default parameters that are used. For example, a defaultforecasting algorithm might be used or a default confidence level (e.g.,95%) might be used for forecasting data.

Upon the prediction analysis tool 1916 receiving or identifying arequest to initiate prediction analysis (e.g., via a predict command),the prediction analysis tool 1916 can parse the request to performconcurrent forecasting for multiple time series data sets. A concurrentforecasting request can be parsed to identify specific time series datasets for which forecasting is to be performed. Further, the concurrentforecasting request (e.g., predict command) can be parsed to identifyvarious parameters and options related to forecasting, such as, forexample, a prediction time span, an amount of historical data to use forforecasting, a forecasting algorithm to utilize, etc. In some cases, apredict command includes multiple time series data sets with each timeseries data sets and the corresponding parameter(s) ending before a newtime series data set and corresponding parameter(s) begins. Whenparameters are not specified, a default parameter can be used.

By way of example only, assume that a request to perform concurrentforecasting includes an indication of five time series data sets. Insuch a case, the concurrent forecasting request can be parsed toidentify the five time series data sets. In addition to identifyingspecific time series data sets for which to perform concurrentforecasting, concurrent forecasting requests can also be parsed toidentify other parameters for each time series, such as an algorithmdesired to be applied in association with the time series data set, etc.

In accordance with identifying the time series data sets for whichconcurrent forecasting is desired, the prediction analysis tool 1916 cangenerate objects to facilitate execution of the specified forecastingalgorithms. The number of objects generated generally corresponds withthe number of time series data sets such that forecasting algorithms canbe separately and independently executed for each time series data set.Stated differently, an object is created for each time series data set.Each object can then independently forecast data in association with thecorresponding time series data set. By way of example only, assumeconcurrent forecasting is to be performed for five different time seriesdata sets. In such a case, five objects are generated or initiated, witheach object performing data forecasting for the corresponding timeseries data set. An object generally refers to a variable(s), a datastructure(s), and/or a function(s) (a subroutine is a sequence ofprogram instructions that perform a specific task). In some cases, anobject refers to a particular instance of a class where the object canbe a combination of variables, functions, and data structures.

In some cases, a parser reads a search query having a predict commandand recognizes the multiple time series data sets and correspondingparameters. Software components, or objects, are then constructed foreach time series data set. Each object can include the relevantinformation about a time series data set such as the values and theparameters to be used. Utilizing separate objects can ensure thatparameters and/or data for one time series data set do not get mixed upwith those associated with another time series data set. Further, usingseparate objects can ensure that various programming states that ariseduring processing do not get mixed up with those associated with anothertime series data set.

As can be appreciated, each object can execute prediction analysis inany manner. For instance, each object might execute prediction analysisas described above. In this manner, the prediction analysis tool 1916can access the appropriate time series data set for use in performingthe prediction of expected outcomes. The appropriate time series dataset to analyze might be indicated, for instance, in the predict commandinput by a user. The time series data set may be referenced and used togenerate predicted missing values within the data set. Upon generatingpredicted missing values, the predicted missing values can be used alongwith the observed time series data to determine periodicity associatedwith the data. The determined periodicity can be used in associationwith the forecasting model associated with the object to forecast orpredict values expected to occur in the future. In this regard, theselected forecasting model can be utilized in association with theparticular time series data set to forecast data.

Each object or forecasting analysis concurrently performed or executedcan then provide forecasted data as output. The forecasted data outputfor each of the concurrent forecasting analyses can be provided to theclient device 1904. As can be appreciated, in some cases, prior tooutputting forecasting data, the data can be combined, aggregated,summarized, or otherwise used to perform calculations to provideresults. For instance, upon each object or forecasting analysis beingconcurrently executed for multiple time series data sets, the predictionanalysis tool 1916 can combine the resulting forecasted data andtransmit to the requesting client device, such as client device 1904. Inthis regard, the forecasted data can be transmitted in association withone another for concurrent display on a display screen.

By way of example only, FIG. 20 provides a user interface illustratingforecasted data associated with concurrent forecasting analyses. Theresulting predicted missing values and/or forecasted expected values formultiple time series data sets can be provided by the predictionanalysis tool 1916 for presentation to the user, for example, via theclient device. FIG. 20 illustrates a captured time series data set for“count(Fin)” 2030 and a captured time series data set for “Nor” 2032. Inaccordance with entering the predict command 2002, predicted missingvalues 2034 for time series data set 2030 and predicted missing values2036 for time series data set 2032 are calculated and presented tovisually illustrate the predicted missing values that occur between theexisting recorded time series data set and/or to predict future values.Further, as illustrated in FIG. 20, predictions associated withconfidence levels can also be included within a forecasting chart orgraph. For example, lines 2038 and 2040 represent lower and upperconfidence levels (95%) for predictions related to the time series dataset “count(Fin)” 2030. Similarly, lines 2042 and 2044 represent lowerand upper confidence levels (95%) for predictions related to the timeseries data set “Nor” 2032. Predicted data can additionally oralternatively be presented in a tabular form 2046.

3.2 Illustrative Time Series Prediction Operations

FIGS. 21-25 illustrate various methods of forecasting data, inaccordance with embodiments of the present invention. Although themethod 2100 of FIG. 21, the method 2200 of FIG. 22, the method 2300 ofFIG. 23, the method 2400 of FIG. 24, and the method 2500 of FIG. 25 areprovided as separate methods, the methods, or aspects thereof, can becombined into a single method or combination of methods. As can beappreciated, additional or alternative steps may also be included indifferent embodiments.

With initial reference to FIG. 21, FIG. 21 illustrates a method ofcomputing periodicity utilizing predicted missing values, in accordancewith embodiments of the present invention. Such a method may beperformed, for example, at a data analysis tool, such as data analysistool 1916 of FIG. 19. Initially, at block 2102, a set of time seriesdata is referenced. The set of time series data might be referenced, forexample, from a data store based on an indication of the particular setof time series data in a predict command. In embodiments, a set of timeseries data determined from raw machine data is received. At block 2104,a determination is made that the time series data has at least onemissing data value. Such a determination may be made, for instance,based on omission of one or more data values within the set of timeseries data. Subsequently, at block, 2106, a predicted missing value isgenerated for each of the at least one missing data values. Thepredicted missing value for a missing data value can be generated basedon a weighted average of a time series data value preceding the missingdata value and a time series data value following the missing datavalue. In embodiments, because several consecutive data values might bemissing, it is not necessary that the preceding and following timeseries data values be immediately adjacent to the missing value. Rather,such time series data values might be the nearest preceding andfollowing time series data values. Further, the value used as thepreceding or following time series data may, in some cases, be acalculation of the value. For instance, assume the data valueimmediately preceding a missing value is 5 and the value collected priorto that is 4. In such a case, the preceding value may be determined tobe an average of the two preceding values, that is 4.5, which can thenbe used to determining the missing value. The set of time series dataand the predicted missing values for each of the at least one missingdata values are used to determine periodicity associated with the set oftime series data. This is shown at block 2008.

Turning now to FIG. 22, FIG. 22 illustrates another method of computingperiodicity utilizing predicted missing values, in accordance withembodiments of the present invention. Such a method may be performed,for example, at a data analysis tool, such as data analysis tool 1916 ofFIG. 19. Initially, at block 2202, a set of time series data isreferenced. The set of time series data might be referenced, forexample, from a data store based on an indication of the particular setof time series data in a predict command. At block 2204, a determinationis made that the time series data has at least one missing data value.Such a determination may be made, for instance, based on omission of oneor more data values within the set of time series data. Subsequently, atblock, 2206, a predicted missing value is generated for each of the atleast one missing data values. The predicted missing value for a missingdata value can be generated based on a weighted average of a time seriesdata value preceding the missing data value and a time series data valuefollowing the missing data value. At block 2208, a complete set ofvalues is generated by aggregating the observed time series data and thegenerated predicted missing values. At block 2210, the complete set ofvalues is used to determine autocorrelations of a plurality of lagsassociated with the complete set of values. In some embodiments, thenumber of lags for which autocorrelations are determined can be limitedto a threshold number (e.g., 2000). As can be appreciated, such lags canbegin at the first data value and capture a first group of values, beginat a data value that results in the last lag (e.g., lag 2000) beingassociated with the final value, or anywhere in between. At block 2212,the greatest or largest autocorrelation is determined to be greater thanan autocorrelation threshold. Based on the largest autocorrelation beinggreater than an autocorrelation threshold, at block 2214, a lagcorresponding with a greatest autocorrelation is identified. Such a lagis then designated as periodicity associated with the complete set ofvalues, as indicated at block 2216. At block 2218, the periodicity isused to generate a forecasting model that predicts outcomes expected inthe future.

With reference to FIG. 23, FIG. 23 illustrates a method of predictingexpected values, in accordance with embodiments of the presentinvention. Such a method may be performed, for example, at a dataanalysis tool, such as data analysis tool 1916 of FIG. 19. Initially, atblock 2302, a data set is converted to a set of time series data. Atblock 2304, a predict command is initiated. Such a predict command maybe initiated, for instance, by a user of a client device. Uponinitiating the predict command, the set of time series data is read todetect one or more missing values. At block 2306, predicted missingvalues are forecasted for the one or more missing values to generate acomplete set of data. Predicted missing values can be forecasted usingan average of neighboring time series values. At block 2308, thecomplete set of data is used to determine a periodicity associated withthe complete set of data. The periodicity is used to generate at leastone forecasting model that is used to predict outcomes expected in thefuture, as indicated at block 2310. Subsequently, at block 2312, the atleast one forecasting model is executed to predict one or more valuesexpected to occur in the future.

Turning to FIG. 24, FIG. 24 illustrates a method of concurrentlypredicting expected values, in accordance with embodiments of thepresent invention. Such a method may be performed, for example, at aclient device, such as client device 1904 of FIG. 19. Initially, atblock 2402, a request for concurrent prediction analysis is received viaa graphical user interface, such as a search graphical user interface.In particular, a user may input or select a command to initiateconcurrent prediction analysis of multiple time series data sets. Suchan input or selection can include an indication of the multiple timeseries data sets, one or more algorithms to utilize in executeforecasting of expected values for the time series data sets, and/orother parameters used to execute forecasting of expected values for thetime series data sets. In response to the request for concurrentprediction analysis, forecasted or predicted values associated with themultiple time series data sets are presented on a display screen of aclient device, as indicated at block 2404. In embodiments, forecastedvalues associated with each time series data set are concurrently orsimultaneously presented on the display screen. For instance, with briefreference to FIG. 20, forecasted values for the “count(Fin)” time seriesdata set and the “Nor” time series data set are concurrently displayed.In other embodiments, the forecasted values can be independentlydisplayed, for instance, by toggling through forecasted values or byselection of a time series data set for which forecasted values aredesired to be viewed.

With reference to FIG. 25, FIG. 25 illustrates a method of predictingexpected values, in accordance with embodiments of the presentinvention. Such a method may be performed, for example, at a dataanalysis tool, such as data analysis tool 1916 of FIG. 19. Initially, atblock 2502, a request for prediction analysis is received. For instance,a user at a client device may input a predict command indicatingmultiple time series data sets that, when entered, are received at acomponent performing forecasting or prediction analysis. Althoughgenerally described herein as a single request for prediction analysis,as can be appreciated, in some cases, multiple requests for predictionanalysis can be communicated. At block 2504, the request for predictionanalysis is parsed to identify an indication of time series data set(s)for which prediction of expected values is desired. At block 2506, it isdetermined whether the request includes an indication of multiple timeseries data sets. If not, the single time series data set specified inthe request is processed in accordance with any corresponding parametersto forecast expected values based on the time series data set, as shownat block 2508. On the other hand, if it is determined that multiple timeseries data sets are included in the request, at block 2510, an objectis initiated or generated for forecasting data in association with eachtime series data set. In other words, for each designated time seriesdata set, an object or prediction analysis is separately initiated orgenerated to independently forecast values expected in light of thecorresponding time series data set. At block 2512, each object orprediction analysis is concurrently executed to determine forecastedvalues corresponding with the time series data set. In particular, aforecasting algorithm specified or designated for a particular timeseries data set can be referenced and used to identify one or moreexpected values. At block 2514, the forecasted values corresponding witheach time series data set are provided, for example, to a client devicesuch as client device 1904 of FIG. 19. In some cases, the forecastedvalues generated from each object or prediction analysis can be combinedand provided as a single output. In other cases, the forecasted valuesgenerated from each object or prediction analysis can be separatelyoutput. The forecasted values can then be presented for display, forinstance, via a client device, e.g., such that a user can concurrentlyor simultaneously view the forecasted values for each of theconcurrently processed time series data sets.

3.3 Illustrative Hardware System

The systems and methods described above may be implemented in a numberof ways. One such implementation includes computer devices havingvarious electronic components. For example, components of the system inFIG. 19 may, individually or collectively, be implemented with deviceshaving one or more Application Specific Integrated Circuits (ASICs)adapted to perform some or all of the applicable functions in hardware.Alternatively, the functions may be performed by one or more otherprocessing units (or cores), on one or more integrated circuits orprocessors in programmed computers. In other embodiments, other types ofintegrated circuits may be used (e.g., Structured/Platform ASICs, FieldProgrammable Gate Arrays (FPGAs), and other Semi-Custom ICs), which maybe programmed in any manner known in the art. The functions of each unitmay also be implemented, in whole or in part, with instructions embodiedin a memory, formatted to be executed by one or more general orapplication-specific computer processors.

An example operating environment in which embodiments of the presentinvention may be implemented is described below in order to provide ageneral context for various aspects of the present invention. Referringto FIG. 26, an illustrative operating environment for implementingembodiments of the present invention is shown and designated generallyas computing device 2600. Computing device 2600 is but one example of asuitable operating environment and is not intended to suggest anylimitation as to the scope of use or functionality of the invention.Neither should the computing device 2600 be interpreted as having anydependency or requirement relating to any one or combination ofcomponents illustrated.

The invention may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Theinvention may be practiced in a variety of system configurations,including handheld devices, consumer electronics, general-purposecomputers, more specialized computing devices, etc. The invention mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

With reference to FIG. 26, computing device 2600 includes a bus 2610that directly or indirectly couples the following devices: memory 2612,one or more processors 2614, one or more presentation components 2616,input/output (I/O) ports 2618, I/O components 2620, and an illustrativepower supply 2622. Bus 2610 represents what may be one or more busses(such as, for example, an address bus, data bus, or combinationthereof). Although depicted in FIG. 26, for the sake of clarity, asdelineated boxes that depict groups of devices without overlap betweenthese groups of devices, in reality, this delineation is not so clearcut and a device may well fall within multiple ones of these depictedboxes. For example, one may consider a display to be one of the one ormore presentation components 2616 while also being one of the I/Ocomponents 2620. As another example, processors have memory integratedtherewith in the form of cache; however, there is no overlap depictedbetween the one or more processors 2614 and the memory 2612. A person ofskill in the art will readily recognize that such is the nature of theart, and it is reiterated that the diagram of FIG. 26 merely depicts anillustrative computing device that can be used in connection with one ormore embodiments of the present invention. It should also be noticedthat distinction is not made between such categories as “workstation,”“server,” “laptop,” “handheld device,” etc., as all such devices arecontemplated to be within the scope of computing device 2600 of FIG. 26and any other reference to “computing device,” unless the contextclearly indicates otherwise.

Computing device 2600 typically includes a variety of computer-readablemedia. Computer-readable media can be any available media that can beaccessed by computing device 2600 and includes both volatile andnonvolatile media, and removable and non-removable media. By way ofexample, and not limitation, computer-readable media may comprisecomputer storage media and communication media. Computer storage mediaincludes both volatile and nonvolatile, removable and non-removablemedia implemented in any method or technology for storage of informationsuch as computer-readable instructions, data structures, programmodules, or other data. Computer storage media includes, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to storethe desired information and which can be accessed by computing device2600. Computer storage media does not comprise signals per se, such as,for example, a carrier wave. Communication media typically embodiescomputer-readable instructions, data structures, program modules, orother data in a modulated data signal such as a carrier wave or othertransport mechanism and includes any information delivery media. Theterm “modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia includes wired media such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared, and otherwireless media. Combinations of any of the above should also be includedwithin the scope of computer-readable media.

Memory 2612 includes computer storage media in the form of volatileand/or nonvolatile memory. The memory may be removable, non-removable,or a combination thereof. Typical hardware devices may include, forexample, solid-state memory, hard drives, optical-disc drives, etc.Computing device 2600 includes one or more processors 2614 that readdata from various entities such as memory 2612 or I/O components 2620.Presentation component(s) 2616 present data indications to a user orother device. Illustrative presentation components include a displaydevice, speaker, printing component, vibrating component, etc.

I/O ports 2618 allow computing device 2600 to be logically coupled toother devices including I/O components 2620, some of which may be builtin. Illustrative components include a keyboard, mouse, microphone,joystick, game pad, satellite dish, scanner, printer, wireless device,etc. The I/O components 2620 may provide a natural user interface (NUI)that processes air gestures, voice, or other physiological inputsgenerated by a user. In some instances, inputs may be transmitted to anappropriate network element for further processing. An NUI may implementany combination of speech recognition, stylus recognition, facialrecognition, biometric recognition, gesture recognition both on screenand adjacent to the screen, air gestures, head and eye tracking, andtouch recognition (as described elsewhere herein) associated with adisplay of the computing device 2600. The computing device 2600 may beequipped with depth cameras, such as stereoscopic camera systems,infrared camera systems, RGB camera systems, touchscreen technology, andcombinations of these, for gesture detection and recognition.Additionally, the computing device 2600 may be equipped withaccelerometers or gyroscopes that enable detection of motion.

As can be understood, implementations of the present disclosure providefor various approaches to data processing. The present invention hasbeen described in relation to particular embodiments, which are intendedin all respects to be illustrative rather than restrictive. Alternativeembodiments will become apparent to those of ordinary skill in the artto which the present invention pertains without departing from itsscope.

From the foregoing, it will be seen that this invention is one welladapted to attain all the ends and objects set forth above, togetherwith other advantages which are obvious and inherent to the system andmethod. It will be understood that certain features and subcombinationsare of utility and may be employed without reference to other featuresand subcombinations. This is contemplated by and is within the scope ofthe claims.

What is claimed is:
 1. A computer-implemented method comprising:receiving a request to perform a predictive analysis in association withmultiple time series data sets and a set of forecasting algorithms toforecast values expected to occur in the future based on thecorresponding time series data set, each of the multiple time seriesdata sets and the set of forecasting algorithms identified in therequest; concurrently forecasting values expected to occur in the futurefor each of the multiple time series data sets using the set offorecasting algorithms identified in the received request; and providingthe forecasted values expected to occur in the future for concurrentdisplay with the corresponding time series data set.
 2. Thecomputer-implemented method of claim 1 further comprising converting aset of raw data to the multiple time series data sets.
 3. Thecomputer-implemented method of claim 1, wherein the request to performthe predictive analysis comprises a predict command that includesseparate indications of each of the multiple time series data sets. 4.The computer-implemented method of claim 1, wherein the request toperform the predictive analysis comprises an indication of a first timeseries data set and a first forecasting algorithm to utilize to performthe predictive analysis based on the first time series data set, and anindication of a second time series data set and a second forecastingalgorithm to utilize to perform the predictive analysis based on thesecond time series data set.
 5. The computer-implemented method of claim1, wherein the request to perform the predictive analysis comprises anindication of a first time series data set, an indication of a secondtime series data set, and an indication of a forecasting algorithm toutilize to perform the predictive analysis in association with the firsttime series data set and the second time series data set.
 6. Thecomputer-implemented method of claim 1, wherein the request to performthe predictive analysis comprises an indication of a first time seriesdata set and first corresponding parameters to utilize to perform thepredictive analysis in association with the first time series data set,and an indication of a second time series data set and secondcorresponding parameters to utilize to perform the predictive analysisin association with the second time series data set.
 7. Thecomputer-implemented method of claim 1 further comprising parsing therequest to identify each of the multiple time series data sets and theset of forecasting algorithms to use in performing the predictiveanalysis.
 8. The computer-implemented method of claim 1 furthercomprising parsing the request to identify a first forecasting algorithmassociated with a first time series data set and a second forecastingalgorithm associated with a second time series data set.
 9. Thecomputer-implemented method of claim 1, wherein the concurrentforecasting occurs via executable objects comprising separate andindependent instances of a class, and wherein initiating an executableobject for each time series data set comprises constructing a differentinstance of the class for each time series data set.
 10. Thecomputer-implemented method of claim 1, wherein the concurrentforecasting occurs via executable objects, and wherein initiating anexecutable object for each time series data set comprises creating adifferent instance of a class for each time series data set andincluding in each instance of the class a corresponding set offorecasting parameters associated with the corresponding forecastingalgorithm.
 11. The computer-implemented method of claim 1, wherein theconcurrent forecasting occurs via executable objects executingseparately and independently from one another.
 12. Thecomputer-implemented method of claim 1, further comprising: accessingthe multiple time series data sets; and concurrently applying the set offorecasting algorithms designated for the corresponding time series dataset to generate the forecasted values.
 13. The computer-implementedmethod of claim 1, wherein the forecasted values are concurrentlypresented as a graphical visualization in connection with thecorresponding time series data sets.
 14. The computer-implemented methodof claim 1, wherein forecasted values associated with each of thecorresponding time series data sets are concurrently presented in atabular format.
 15. One or more non-transitory computer-readable storagemedia having instructions stored thereon, wherein the instructions, whenexecuted by a computing device, cause the computing device to: receivinga request to perform a predictive analysis in association with multipletime series data sets and a set of forecasting algorithms to forecastvalues expected to occur in the future based on the corresponding timeseries data set, each of the multiple time series data sets and the setof forecasting algorithms identified in the request; concurrentlyforecasting values expected to occur in the future for each of themultiple time series data sets using the set of forecasting algorithmsidentified in the received request; and providing the forecasted valuesexpected to occur in the future for concurrent display with thecorresponding time series data set.
 16. A computing device comprising:one or more processors; and a memory coupled with the one or moreprocessors, the memory having instructions stored thereon, wherein theinstructions, when executed by the one or more processors, cause thecomputing device to: receiving a request to perform a predictiveanalysis in association with multiple time series data sets and a set offorecasting algorithms to forecast values expected to occur in thefuture based on the corresponding time series data set, each of themultiple time series data sets and the set of forecasting algorithmsidentified in the request; concurrently forecasting values expected tooccur in the future for each of the multiple time series data sets usingthe set of forecasting algorithms identified in the received request;and providing the forecasted values expected to occur in the future forconcurrent display with the corresponding time series data set.
 17. Thecomputing device of claim 16, wherein the request to perform thepredictive analysis comprises a predict command that includes separateindications of each of the multiple time series data sets.
 18. Thecomputing device of claim 16, wherein the request to perform thepredictive analysis comprises an indication of a first time series dataset and a first forecasting algorithm to utilize to perform thepredictive analysis based on the first time series data set, and anindication of a second time series data set and a second forecastingalgorithm to utilize to perform the predictive analysis based on thesecond time series data set.
 19. The computing device of claim 16,wherein the request to perform the predictive analysis comprises anindication of a first time series data set, an indication of a secondtime series data set, and an indication of a forecasting algorithm toutilize to perform the predictive analysis in association with the firsttime series data set and the second time series data set.
 20. Thecomputing device of claim 16, further comprising: accessing the multipletime series data sets; and concurrently applying the set of forecastingalgorithms designated for the corresponding time series data set togenerate the forecasted values.